Learning redundant dictionaries with translation invariance property: the MoTIF algorithm - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2005

Learning redundant dictionaries with translation invariance property: the MoTIF algorithm

Abstract

Sparse approximation using redundant dictionaries is an efficient tool for many applications in the field of signal processing. The performances largely depend on the adaptation of the dictionary to the signal to decompose. As the statistical dependencies are most of the time not obvious in natural high- dimensional data, learning fundamental patterns is an alternative to analytical design of bases and has become a field of acute research. Most of the time, the underlying patterns of a class of signals can be found at any time, and in the design of a dictionary, this translation invariance property should be present. We present a new algorithm for learning short generating functions, each of them building a set of atoms corresponding to all its translations. The resulting dictionary is highly redundant and translation invariant.
Fichier principal
Vignette du fichier
SPARS_05_-_Workshop_on_Signal_Processing_with_Adaptive_Sparse_Structured_Representations_2005_Jost.pdf (163.79 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00816804 , version 1 (23-04-2013)

Identifiers

  • HAL Id : hal-00816804 , version 1

Cite

Philippe Jost, Pierre Vandergheynst, Sylvain Lesage, Rémi Gribonval. Learning redundant dictionaries with translation invariance property: the MoTIF algorithm. SPARS'05 - Workshop on Signal Processing with Adaptive Sparse Structured Representations, Inria, 2005, Rennes, France. pp.1-3. ⟨hal-00816804⟩
272 View
480 Download

Share

Gmail Facebook X LinkedIn More